Overview

Dataset statistics

Number of variables11
Number of observations608
Missing cells40
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.4 KiB
Average record size in memory88.2 B

Variable types

NUM8
CAT3

Warnings

Hand has constant value "608" Constant
ASF is highly correlated with eTIVHigh correlation
eTIV is highly correlated with ASFHigh correlation
SES has 38 (6.2%) missing values Missing

Reproduction

Analysis started2021-11-24 19:50:08.506179
Analysis finished2021-11-24 19:50:33.883731
Duration25.38 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct399
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.84375
Minimum0
Maximum415
Zeros2
Zeros (%)0.3%
Memory size4.8 KiB
2021-11-24T14:50:33.998229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q197.75
median196.5
Q3290.25
95-th percentile367.65
Maximum415
Range415
Interquartile range (IQR)192.5

Descriptive statistics

Standard deviation113.4730745
Coefficient of variation (CV)0.5823798532
Kurtosis-1.135586136
Mean194.84375
Median Absolute Deviation (MAD)96
Skewness0.01724620701
Sum118465
Variance12876.13864
MonotocityNot monotonic
2021-11-24T14:50:34.340732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
020.3%
 
22820.3%
 
24320.3%
 
24220.3%
 
24120.3%
 
24020.3%
 
23920.3%
 
8120.3%
 
23620.3%
 
23520.3%
 
Other values (389)58896.7%
 
ValueCountFrequency (%) 
020.3%
 
120.3%
 
220.3%
 
310.2%
 
410.2%
 
ValueCountFrequency (%) 
41510.2%
 
41410.2%
 
41310.2%
 
41210.2%
 
41110.2%
 

M/F
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
F
369 
M
239 
ValueCountFrequency (%) 
F36960.7%
 
M23939.3%
 
2021-11-24T14:50:34.640731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-24T14:50:34.757231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:35.019236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Hand
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
R
608 
ValueCountFrequency (%) 
R608100.0%
 
2021-11-24T14:50:35.295233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-24T14:50:35.427233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:35.697740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Age
Real number (ℝ≥0)

Distinct54
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.20888158
Minimum33
Maximum98
Zeros0
Zeros (%)0.0%
Memory size4.8 KiB
2021-11-24T14:50:35.917231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile58
Q170
median76
Q382
95-th percentile90
Maximum98
Range65
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.86502584
Coefficient of variation (CV)0.1311683625
Kurtosis1.160860294
Mean75.20888158
Median Absolute Deviation (MAD)6
Skewness-0.727201952
Sum45727
Variance97.31873483
MonotocityNot monotonic
2021-11-24T14:50:36.141734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
73406.6%
 
80345.6%
 
78325.3%
 
71304.9%
 
75304.9%
 
81274.4%
 
77233.8%
 
82213.5%
 
74213.5%
 
69203.3%
 
Other values (44)33054.3%
 
ValueCountFrequency (%) 
3310.2%
 
3910.2%
 
4310.2%
 
4510.2%
 
4630.5%
 
ValueCountFrequency (%) 
9810.2%
 
9710.2%
 
9620.3%
 
9510.2%
 
9420.3%
 

Educ
Real number (ℝ≥0)

Distinct17
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.18421053
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Memory size4.8 KiB
2021-11-24T14:50:36.596232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median12
Q316
95-th percentile18
Maximum23
Range22
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.058388118
Coefficient of variation (CV)0.5948804871
Kurtosis-1.460534697
Mean10.18421053
Median Absolute Deviation (MAD)6
Skewness-0.1238958173
Sum6192
Variance36.70406659
MonotocityNot monotonic
2021-11-24T14:50:36.815730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
1210316.9%
 
168113.3%
 
186410.5%
 
26410.5%
 
5518.4%
 
4508.2%
 
3477.7%
 
14335.4%
 
13274.4%
 
1233.8%
 
Other values (7)6510.7%
 
ValueCountFrequency (%) 
1233.8%
 
26410.5%
 
3477.7%
 
4508.2%
 
5518.4%
 
ValueCountFrequency (%) 
2330.5%
 
20132.1%
 
186410.5%
 
1791.5%
 
168113.3%
 

SES
Real number (ℝ≥0)

MISSING

Distinct5
Distinct (%)0.9%
Missing38
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean2.471929825
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size4.8 KiB
2021-11-24T14:50:36.962229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.128050153
Coefficient of variation (CV)0.4563439228
Kurtosis-1.101423586
Mean2.471929825
Median Absolute Deviation (MAD)1
Skewness0.19902364
Sum1409
Variance1.272497148
MonotocityNot monotonic
2021-11-24T14:50:37.175233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
216827.6%
 
113822.7%
 
313121.5%
 
412320.2%
 
5101.6%
 
(Missing)386.2%
 
ValueCountFrequency (%) 
113822.7%
 
216827.6%
 
313121.5%
 
412320.2%
 
5101.6%
 
ValueCountFrequency (%) 
5101.6%
 
412320.2%
 
313121.5%
 
216827.6%
 
113822.7%
 

MMSE
Real number (ℝ≥0)

Distinct19
Distinct (%)3.1%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean27.23432343
Minimum4
Maximum30
Zeros0
Zeros (%)0.0%
Memory size4.8 KiB
2021-11-24T14:50:37.380230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile20
Q126
median29
Q330
95-th percentile30
Maximum30
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.687980493
Coefficient of variation (CV)0.1354166371
Kurtosis5.235330036
Mean27.23432343
Median Absolute Deviation (MAD)1
Skewness-2.0613576
Sum16504
Variance13.60120012
MonotocityNot monotonic
2021-11-24T14:50:37.559731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
3018330.1%
 
2914323.5%
 
287412.2%
 
27508.2%
 
26376.1%
 
23213.5%
 
21172.8%
 
25152.5%
 
20142.3%
 
22142.3%
 
Other values (9)386.2%
 
ValueCountFrequency (%) 
410.2%
 
710.2%
 
1410.2%
 
1561.0%
 
1640.7%
 
ValueCountFrequency (%) 
3018330.1%
 
2914323.5%
 
287412.2%
 
27508.2%
 
26376.1%
 

CDR
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
0
341 
0.5
193 
1
69 
2
 
5
ValueCountFrequency (%) 
034156.1%
 
0.519331.7%
 
16911.3%
 
250.8%
 
2021-11-24T14:50:37.738733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-24T14:50:37.862234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:38.006233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

eTIV
Real number (ℝ≥0)

HIGH CORRELATION

Distinct343
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1477.0625
Minimum1106
Maximum2004
Zeros0
Zeros (%)0.0%
Memory size4.8 KiB
2021-11-24T14:50:38.167236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1106
5-th percentile1230
Q11352.5
median1460
Q31569
95-th percentile1800.55
Maximum2004
Range898
Interquartile range (IQR)216.5

Descriptive statistics

Standard deviation170.6537948
Coefficient of variation (CV)0.1155359335
Kurtosis-0.008311260658
Mean1477.0625
Median Absolute Deviation (MAD)109
Skewness0.5450006412
Sum898054
Variance29122.71767
MonotocityNot monotonic
2021-11-24T14:50:38.373735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
147571.2%
 
149561.0%
 
150650.8%
 
129550.8%
 
153650.8%
 
156950.8%
 
148350.8%
 
145350.8%
 
139050.8%
 
144740.7%
 
Other values (333)55691.4%
 
ValueCountFrequency (%) 
110610.2%
 
112320.3%
 
114310.2%
 
114710.2%
 
115110.2%
 
ValueCountFrequency (%) 
200410.2%
 
199210.2%
 
198710.2%
 
195710.2%
 
193110.2%
 

nWBV
Real number (ℝ≥0)

Distinct176
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7371299342
Minimum0.644
Maximum0.847
Zeros0
Zeros (%)0.0%
Memory size4.8 KiB
2021-11-24T14:50:38.574234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.644
5-th percentile0.67335
Q10.704
median0.736
Q30.76625
95-th percentile0.81465
Maximum0.847
Range0.203
Interquartile range (IQR)0.06225

Descriptive statistics

Standard deviation0.0426701987
Coefficient of variation (CV)0.05788694329
Kurtosis-0.5037719532
Mean0.7371299342
Median Absolute Deviation (MAD)0.031
Skewness0.2655852126
Sum448.175
Variance0.001820745857
MonotocityNot monotonic
2021-11-24T14:50:38.766232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.739132.1%
 
0.696111.8%
 
0.727101.6%
 
0.69591.5%
 
0.73791.5%
 
0.74891.5%
 
0.71591.5%
 
0.70381.3%
 
0.70581.3%
 
0.73681.3%
 
Other values (166)51484.5%
 
ValueCountFrequency (%) 
0.64430.5%
 
0.64510.2%
 
0.64610.2%
 
0.65210.2%
 
0.65310.2%
 
ValueCountFrequency (%) 
0.84710.2%
 
0.84110.2%
 
0.83910.2%
 
0.83810.2%
 
0.83710.2%
 

ASF
Real number (ℝ≥0)

HIGH CORRELATION

Distinct314
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.203597039
Minimum0.876
Maximum1.587
Zeros0
Zeros (%)0.0%
Memory size4.8 KiB
2021-11-24T14:50:38.971734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.876
5-th percentile0.9744
Q11.118
median1.202
Q31.2975
95-th percentile1.427
Maximum1.587
Range0.711
Interquartile range (IQR)0.1795

Descriptive statistics

Standard deviation0.1350911235
Coefficient of variation (CV)0.1122394947
Kurtosis-0.2205396707
Mean1.203597039
Median Absolute Deviation (MAD)0.094
Skewness0.03546645827
Sum731.787
Variance0.01824961166
MonotocityNot monotonic
2021-11-24T14:50:39.182229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.1991.5%
 
1.17471.2%
 
1.13461.0%
 
1.02461.0%
 
1.20250.8%
 
1.20850.8%
 
1.18350.8%
 
1.25550.8%
 
1.18450.8%
 
1.29150.8%
 
Other values (304)55090.5%
 
ValueCountFrequency (%) 
0.87610.2%
 
0.88110.2%
 
0.88310.2%
 
0.89710.2%
 
0.90910.2%
 
ValueCountFrequency (%) 
1.58710.2%
 
1.56320.3%
 
1.53510.2%
 
1.53110.2%
 
1.52510.2%
 

Interactions

2021-11-24T14:50:14.618097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:15.142709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:15.351711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:15.637708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:15.851712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:16.054211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:16.277208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:16.491709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:16.916219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:17.204208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:17.453717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:17.756209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:17.977217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:18.270211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:18.585219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:18.890711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:19.194215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:19.400208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:19.607710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:19.844716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:20.182213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:20.360706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:20.527706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:20.729707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:21.092213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:21.345707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:21.577708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:21.772210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:21.981215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:22.179722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:22.424716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:22.865721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:23.283210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:23.499212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:23.823710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:24.089211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:24.328208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:24.601212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:24.980215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:25.397207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:25.681219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:26.001715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:26.263708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:26.414211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:26.609208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:26.848708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:27.032217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:27.372207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:27.692215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:27.912711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:28.147713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:28.429211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:28.678708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:29.068710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:29.562709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:29.834215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:30.108706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:30.399212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:30.675706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:30.957711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:31.329206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:31.619211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:32.062719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:32.438214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-11-24T14:50:39.407730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-24T14:50:39.781732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-24T14:50:40.053231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-24T14:50:40.590734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-11-24T14:50:40.875731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-24T14:50:32.881710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:33.386210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:33.615733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-24T14:50:33.750230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexM/FHandAgeEducSESMMSECDReTIVnWBVASF
00FR742.03.029.00.013440.7431.306
11FR554.01.029.00.011470.8101.531
22FR734.03.027.00.514540.7081.207
38MR745.02.030.00.016360.6891.073
49FR523.02.030.00.013210.8271.329
511FR815.02.030.00.016640.6791.055
613MR762.0NaN28.00.517380.7191.010
714MR822.04.027.00.514770.7391.188
816MR393.04.028.00.016360.8131.073
917FR895.01.030.00.015360.7151.142

Last rows

df_indexM/FHandAgeEducSESMMSECDReTIVnWBVASF
598363FR6813.02.030.00.015060.7401.165
599364FR7213.02.030.00.015100.7231.162
600365FR7216.03.024.00.513540.7331.296
601366FR7316.03.021.01.013510.7081.299
602367MR8016.01.028.00.517040.7111.030
603368MR8216.01.028.00.516930.6941.037
604369MR8616.01.026.00.516880.6751.040
605370FR6113.02.030.00.013190.8011.331
606371FR6313.02.030.00.013270.7961.323
607372FR6513.02.030.00.013330.8011.317